The invention relates to condition monitoring of valves in industrial processes.
Condition monitoring of process equipment, such as valves, plays a significant role in undisturbed operation and performance of processes. Faulty valves can lead to unplanned shutdowns of factories and cause significant costs. As a result, different types of condition monitoring systems have been developed for observing the condition of valves, which are based on measurements on the operation of a valve packet performed by intelligent positioners. Intelligent digital valve controllers have brought along a great number of valve performance indicators. They enable a better productivity in maintenance and life cycle management of valves. Since one maintenance organisation may be responsible for numerous, even as many as 5000, valves, it is obvious that an automatic analysis and performance monitoring helps to understand the overall picture, anticipate the need for maintenance and allocate the maintenance operations.
There are various types of condition monitoring methods, and they are often divided, according to the level of knowledge, into model-based and statistical methods. Many condition monitoring applications exist, and they may be divided into general and device-specific applications, for example. A device-specific condition monitoring application is most suitable when a device is critical for the operation and safety of a production plant. Another reason for selecting a device-specific application is the number of installed devices; for instance, there are often so many valves that the best solution is to use a valve-specific condition monitoring application.
Friman M., A New Method for Condition Monitoring of Unit Processes and Field Devices (in Finnish), In: Automation 2003 Seminar, Helsinki Fair Centre 9, —11 Sep. 2003, Society of Automation, Helsinki, 2003, p. 477-482, discloses a statistical condition monitoring method, which has been applied to condition monitoring of pumps. The method employs conditional histograms, which is a common statistical analyzing method, and, if necessary, the operation method and operating point of the process, for instance, are taken into account in the monitoring. The property to be monitored is called a quality variable. The quality variable may be any variable indicating something about the operation of the device, such as electric current of a pump, the measured quality, cost, or a performance variable calculated on the basis of the measurements. Operating point variables are explanatory variables acting on a quality variable. Operating point variables may include, for instance, flow, pressure after the pump, production volume and production rate, type and product number, raw material property, process state, such as idle, start-up and shutdown, or other malfunction or failure, and process stage. The method distinguishes between operating point variables and quality variables. A quality distribution, i.e. a histogram, is generated from a quality variable in a short time range (for instance, the distribution of electric current of a pump in the last 4 hours), whereby the values of the quality variable are divided into a plurality of bins (for example, the electric current of the pump is divided into eleven bins 40, 41, . . . 50A). In addition, operating point specific reference distributions are generated, each of which represents the quality distribution in a group of operating points in a long time period. The operating points are formed by dividing the operating point variables into a plurality of bins (for example, six bins, such as 15, 19, . . . 35 l/s, for flow, and three bins, such as 200, 300, 400 kPa, for pressure after the pump). The operator is shown a momentary quality distribution (such as the electric current distribution of the pump in the last 4 hours) and a reference distribution, to which the operator may compare the momentary distribution. Thus, the pump that functions more poorly than before is immediately revealed at one glance on the basis of the quality and reference distributions differing from one another.
Mats Friman et al., An Analysing and Monitoring Environment of Intelligent Control Valves (in Finnish), In: Automation 2005 Seminar, Helsinki Fair Centre 6. —8 Sep. 2005, discloses a condition monitoring method, wherein the present operation and state of a control valve are compared with a valve-specific model (multi-variable histogram) generated form the history data of the device. Signals describing the present operation and state of the valve may be measurements, such as a deviation and a load factor, or counters, such as valve travel meter and number of reversals. As a result of the comparison, a fuzzy cluster is provided for each signal with classes ‘high’, ‘normal’ and ‘low’. These readings indicate at which level the last observations are compared to a longer-time distribution of the same signal. Fuzzy clusters are supplied to a reasoning mechanism, to which known valve failures are configured, as a high/normal/low combination of different signals. As a result of the reasoning, the condition of the valve and the matching with known faults are estimated. For each valve, one performance-related identification, i.e. a performance index (PI), is calculated, which may also be observed as a trend. The index varies between 0 and 1, whereby the bigger index means a better performance. PI=1 means that the valve is ok, PI=0 means the worst possible performance situation. The trend of the performance index is observed in order to anticipate the occurrence of faults.
Mats Friman et al., Managing Adaptive Process Monitoring: New Tools and Case Examples, Conference: The 15th Mediterranean Conference on Control and Automation (MED'07), Athens, Greece, 2007, discloses a similar condition monitoring based on conditional histograms.